Resulting names are unique and consist only of the _ character, numbers, and letters. Capitalization preferences can be specified using the case parameter.

Accented characters are transliterated to ASCII. For example, an "o" with a German umlaut over it becomes "o", and the Spanish character "enye" becomes "n".

This function takes and returns a data.frame, for ease of piping with `%>%`. For the underlying function that works on a character vector of names, see make_clean_names. clean_names relies on the versatile function to_any_case, which accepts many arguments. See that function's documentation for ideas on getting the most out of clean_names. A few examples are included below.

clean_names(dat, ...)

# S3 method for data.frame
clean_names(dat, ...)

# S3 method for default
clean_names(dat, ...)

# S3 method for sf
clean_names(dat, ...)

# S3 method for tbl_graph
clean_names(dat, ...)

Arguments

dat

the input data.frame.

...

Arguments passed on to make_clean_names

case

The desired target case (default is "snake") will be passed to snakecase::to_any_case() with the exception of "old_janitor", which exists only to support legacy code (it preserves the behavior of clean_names() prior to addition of the "case" argument (janitor versions <= 0.3.1). "old_janitor" is not intended for new code. See to_any_case for a wide variety of supported cases, including "sentence" and "title" case.

replace

A named character vector where the name is replaced by the value.

ascii

Convert the names to ASCII (TRUE, default) or not (FALSE).

use_make_names

Should make.names() be applied to ensure that the output is usable as a name without quoting? (Avoiding make.names() ensures that the output is locale-independent but quoting may be required.)

sep_in

(short for separator input) if character, is interpreted as a regular expression (wrapped internally into stringr::regex()). The default value is a regular expression that matches any sequence of non-alphanumeric values. All matches will be replaced by underscores (additionally to "_" and " ", for which this is always true, even if NULL is supplied). These underscores are used internally to split the strings into substrings and specify the word boundaries.

transliterations

A character vector (if not NULL). The entries of this argument need to be elements of stringi::stri_trans_list() (like "Latin-ASCII", which is often useful) or names of lookup tables (currently only "german" is supported). In the order of the entries the letters of the input string will be transliterated via stringi::stri_trans_general() or replaced via the matches of the lookup table. When named character elements are supplied as part of `transliterations`, anything that matches the names is replaced by the corresponding value. You should use this feature with care in case of case = "parsed", case = "internal_parsing" and case = "none", since for upper case letters, which have transliterations/replacements of length 2, the second letter will be transliterated to lowercase, for example Oe, Ae, Ss, which might not always be what is intended. In this case you can make usage of the option to supply named elements and specify the transliterations yourself.

parsing_option

An integer that will determine the parsing_option.

  • 1: "RRRStudio" -> "RRR_Studio"

  • 2: "RRRStudio" -> "RRRS_tudio"

  • 3: "RRRStudio" -> "RRRSStudio". This will become for example "Rrrstudio" when we convert to lower camel case.

  • -1, -2, -3: These parsing_options's will suppress the conversion after non-alphanumeric values.

  • 0: no parsing

numerals

A character specifying the alignment of numerals ("middle", left, right, asis or tight). I.e. numerals = "left" ensures that no output separator is in front of a digit.

Value

Returns the data.frame with clean names.

Details

clean_names() is intended to be used on data.frames and data.frame-like objects. For this reason there are methods to support using clean_names() on sf and tbl_graph (from tidygraph) objects. For cleaning other named objects like named lists and vectors, use make_clean_names().

Examples

# --- Simple Usage --- x <- data.frame(caseID = 1, DOB = 2, Other = 3) clean_names(x)
#> case_id dob other #> 1 1 2 3
# or pipe in the input data.frame: x %>% clean_names()
#> case_id dob other #> 1 1 2 3
# if you prefer camelCase variable names: x %>% clean_names(., "lower_camel")
#> caseId dob other #> 1 1 2 3
# (not run) run clean_names after reading in a spreadsheet: # library(readxl) # read_excel("messy_excel_file.xlsx") %>% # clean_names() # --- Taking advantage of the underlying snakecase::to_any_case arguments --- # Restore column names to Title Case, e.g., for plotting mtcars %>% clean_names(case = "title")
#> Mpg Cyl Disp Hp Drat Wt Qsec Vs Am Gear Carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Tell clean_names to leave certain abbreviations untouched: x %>% clean_names(case = "upper_camel", abbreviations = c("ID", "DOB"))
#> CaseID DOB Other #> 1 1 2 3